Department of Computer Science, Superior University, Lahore 54600, Pakistan.
Department of Software Engineering, Superior University, Lahore 54600, Pakistan.
Comput Intell Neurosci. 2022 Jul 19;2022:4348235. doi: 10.1155/2022/4348235. eCollection 2022.
Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method's reliability which shows encouraging results.
恶性黑色素瘤如果不治疗，被认为是最致命的皮肤疾病之一。黑色素瘤引起的死亡率超过其他皮肤恶性肿瘤的两倍。这些事实促使计算机科学家寻找自动方法来发现皮肤癌。如今，皮肤科医生广泛使用皮肤图像分析来自动发现疾病的早期阶段。在开发这样的系统时，计算机科学研究人员面临的挑战之一是现有图像的不清晰，例如阴影、对比度低、毛发和镜面反射等噪声，这使得在这些图像中检测皮肤病变变得复杂。本文提出了使用主动轮廓方法解决上述问题的方案。然而，动态轮廓方法中的种子选择存在主要缺点，即应该从何处开始分割过程。本文使用基于高斯滤波器的最大熵和形态处理方法来找到主动轮廓的自动种子点。通过将其纳入，它可以自动从皮肤镜图像中分割病变。我们提出的方法在标准数据集真皮上进行了定量和定性测量，并用于测试所提出方法的可靠性，结果令人鼓舞。
